The impact of macroeconomic variables on Brusa Stock Exchange using machine learning model
Bibliographic record
Abstract
This paper attempts to explore the relationship between four macroeconomic variables on Bursa Stock Exchange using the Machine Learning method. Quarterly data has been used from 2015 quarter 1 until 2022 quarter 4 for all the variables like, overnight policy rate, industrial production index, consumer sentiment index, and unemployment rate. Then, the machine learning method, SHapley Additive Explanation (SHAP) was used to calculate the impact value between stock price and macroeconomic variables. After calculating the impact value, technical indicators were used to test the contribution of macroeconomic variables across the 13 sectors. \n \nResults showed all variables evolve differently in the different phases of the economic cycle. The value of the impact of each sector on the index of industrial production is different. Some sectors show a positive impact value on the overnight policy rate, while the rest of the sectors show the opposite. During the strong economy, almost all sectors show divergence. However, during the pandemic, most of the sectors have had an almost neutral or positive impact on the unemployment rate. Whereas, the consumer sentiment index has an almost neutral impact value on all sectors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".